Amir Ghasemkhani
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Publications

Journal Publications

  • R. Wu, S. D. Hamshaw, L. Yang, D. W. Kincaid, R. Etheridge, A. Ghasemkhani, "Data Imputation for Multivariate Time Series Sensor Data with Large Gaps of Missing Data," Accepted to be published in IEEE Sensors Journal.
  • A. Ghasemkhani, I. Niazazari, Y. Liu, H. Livani, V. Centeno, and L. Yang, "A regularized tensor completion approach for PMU data recovery," IEEE Transactions on Smart Grid, vol. 12, no. 2, pp. 1519-1528, March 2021, doi: 10.1109/TSG.2020.3030566.
  • M. Jafari, A. Ghasemkhani, V. Sarfi, H. Livani, L. Yang and H. Xu, "Biologically inspired adaptive intelligent secondary control for MGs under cyber imperfections," in IET Cyber-Physical Systems: Theory & Applications, vol. 4, no. 4, pp. 341-352, 12 2019, doi: 10.1049/iet-cps.2018.5003.
  • A. Ghasemkhani, L. Yang, and J. Zhang, "Learning-based demand response for privacy-preserving users," in IEEE Transactions on Industrial Informatics, vol. 15, no. 9, pp. 4988-4998, Sept. 2019.
  • A. Ghasemkhani, H. Monsef, A. Rahimi-Kian and A. Anvari-Moghaddam, "Optimal design of a wide area measurement system for improvement of power network monitoring using a dynamic multiobjective shortest path algorithm," in IEEE Systems Journal, vol. 11, no. 4, pp. 2303-2314, Dec. 2017.

Conference Publications

  • S. Perez-Gamboa, Q. Sun, A. Ghasemkhani, "A Wireless Sensor Based Multi-layer Hybrid Deep Learning Model for Highly Correlated Human Activity Recognition," Accepted to be published in IEEE IAS GlobConET 2022.
  • A. Ghasemkhani, Y. Liu, and L. Yang, "Real-time event detection using rank signatures of real-world PMU data," in 2022 IEEE PES General Meeting, Denver, CO, USA, July, 2022.
  • R. Hossain, M. Mansour Lakouraj, A. Ghasemkhani, H. Livani and M. Ben-Idris, "Deep Reinforcement Learning-based Volt-Var Optimization in Distribution Grids with Inverter-based Resources," 2021 North American Power Symposium (NAPS), 2021, pp. 01-06, doi: 10.1109/NAPS52732.2021.9654630.
  • Y. Hou, F. Muheidat, A. Ghasemkhani, Q. Sun, H. Qiao, M. McIntyre, M. Van Wart,"The Adaptation of Online Project-Based Learning in Computer Engineering Education Settings," In International Conference on Interactive Collaborative Learning, vol 390. Springer, Cham, September 2021.
  • I. Niazazari, H. Livani, A. Ghasemkhani, Y. Liu, and L. Yang, "Event cause analysis in distribution networks using synchro waveform measurements," in 2020 North American Power Symposium (NAPS), April 11, 2021.
  • I. Niazazari, Y. Liu, A. Ghasemkhani, S. Biswas, H. Livani, L. Yang, and V. A. Centeno, "PMU-data-driven event classification in power transmission grids," in 2021 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT), February 2021.
  • A. Ghasemkhani, Y. Liu, and L. Yang, "Low-rank tensor completion for PMU data recovery," in 2021 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT), February 2021.
  • Y. Liu, A. Ghasemkhani, L. Yang, J. Zhao, J. Zhang, and V. Vittal, "Seasonal self-evolving neural networks based short-term wind farm generation forecast," in Proc. IEEE SmartGridComm, November 11-13, 2020.
  • A. Ghasemkhani, A. Darvishi, I. Niazazari, A. Darvishi, H. Livani and L. Yang, "DeepGrid: Robust Deep Reinforcement Learning-based Contingency Management," 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 2020, pp. 1-5, doi: 10.1109/ISGT45199.2020.9087633.
  • Jingting Huang, A. Ghasemkhani, Sandra Marcela Loria Salazar, Feng Yan, Lei Yang, Evgenia Smirni, Jens Redemann, Heather Holmes, "Using Novel Machine Learning Algorithms to Improve the Spatiotemporal Coverage of Satellite Aerosol Optical Depth," in AGU Fall Meeting, 2019, Accepted.
  • V. Sarfi, A. Ghasemkhani, I. Niazazari, H. Livani and L. Yang, "Decentralized Dynamic State Estimation with Bimodal Gaussian Mixture Measurement Noise," 2019 North American Power Symposium (NAPS), Wichita, KS, USA, 2019, pp. 1-5, doi: 10.1109/NAPS46351.2019.9000291.
  • M. Jafari, V. Sarfi, A. Ghasemkhani, H. Livani, L. Yang and H. Xu, "Adaptive Intelligent Secondary Control of Microgrids Using a Biologically-Inspired Reinforcement Learning," 2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, USA, 2019, pp. 1-5, doi: 10.1109/PESGM40551.2019.8974103.
  • A. Ghasemkhani and L. Yang, "Reinforcement Learning Based Pricing for Demand Response," 2018 IEEE International Conference on Communications Workshops (ICC Workshops), Kansas City, MO, 2018, pp. 1-6, doi: 10.1109/ICCW.2018.8403783.
  • M. Jafari, V. Sarfi, A. Ghasemkhani, H. Livani, L. Yang, and H. Xu “Adaptive neural network based intelligent secondary control for microgrids,” "Adaptive neural network based intelligent secondary control for microgrids," 2018 IEEE Texas Power and Energy Conference (TPEC), College Station, TX, 2018, pp. 1-6, doi: 10.1109/TPEC.2018.8312064.
  • A. Ghasemkhani, V. Sarfi, L. Yang, and H. Livani, “Decentralized dynamic state estimation with missing and delayed PMU measurements,”2018 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), Denver, CO, 2018, pp. 1-5, doi: 10.1109/TDC.2018.8440475.
  • A. Ghasemkhani, A. Anvari-Moghaddam, J. M. Guerrero, and B. Bak-Jensen, "An efficient Multi-objective approach for designing of communication interfaces in smart grids," Proceedings of IEEE PES Innovative Smart Grid Technologies, Ljubljana, Slovenia (ISGT Europe 2016), Oct. 2016.
  • M. Parvizimosaed, A. Anvari-Moghaddam, A. Ghasemkhani and A. Rahimi-Kian, "Multi-objective dispatch of distributed generations in a grid-connected micro-grid considering demand response actions," in 22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013), Stockholm, 2013, pp. 1-4.